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Scientific experiments are often designed to include a control group along with the experimental group within their experimental design. Both of these groups are identical in all aspects, except that while the experimental group is subjected to the treatment or interventions of the study, the control group is not. Therefore, a control group can be defined as a group of participants that do not receive the treatment within an experiment.
The inclusion of a control group in an experiment’s design can significantly strengthen the findings of a study as it allows the researcher to clearly establish whether or not the treatment being investigated truly has effects on the experimental group. When a control group is used appropriately, it is identical to the treatment group in every other way, the only difference being that the treatment group will receive the treatment. As this is the only difference between the two groups, any change identified in the treatment group, that isn’t identified in the control group, can be clearly attributed to the treatment being studied.
Let’s take an example. If a new experimental drug is being tested as a cure for a disease, the experimental/treatment group will receive the treatment while the control group will receive a placebo or nothing at all. If both the groups are identical in all other aspects, any changes noted in the treatment group can be attributed to the drug being tested.
The use of control groups is critical in experimental design. When researchers want to see how a new treatment affects people, they divide them into at least two groups at random:
The treatment group (also known as the experimental group) gets the treatment that the researcher is interested in.
The control group is given either no treatment, a standard treatment with a recognised effect, or a placebo (a fake treatment).
The treatment is any independent variable that the experimenters manipulate, and the specific form of the treatment depends on the sort of research being conducted. It could be a novel medicine or therapy in a medical trial. It might be a new social policy that some people get and others don’t, according to public policy studies.
Consider a treatment group that receives a vaccination and has a 10% infection rate. You can’t tell if that’s an improvement on its own. However, if you include an unvaccinated control group with a 20% infection rate, you can see that the vaccine improved the outcome by 10%.
The control group indicates the treatment’s effect by providing a baseline for comparison. A control group and at least one treatment group are usually included in most research. In an ideal experiment, all individuals in all groups begin with the same basic features, with the exception that those in the treatment groups are given a therapy. You can ascribe differences after the experiment to the treatments if the groups are otherwise equivalent before treatment begins.
There may be more than one treatment or control group in a study. Researchers may want to look at the effects of many treatments at the same time, or compare a new treatment to several existing options.
Let’s look at a few different types of control groups used in experiments:
A type of control group that is given an inactive substance known as a placebo. The placebo is intended to trigger the “placebo effect” wherein participants respond to the “treatment” (an inactive substance in this case) purely due to the belief that they are being treated.
Double-blinded control group experiments involve the participants and researchers being left unaware of who is in the control group and who is not. This helps eliminate unintentional bias that may be caused by having the knowledge of who belongs to which group.
Unlike the placebo control group, this control group does not receive any placebo. Instead, this group is simply monitored so that its results can be compared with that of the treatment group.
In these types of control group experiments, the control group receives neither treatment nor placebo at the time the experiment is being conducted. However, once the experiment is complete, the waitlist control group receives the treatment or another form of an active ingredient. This is done so that the control group doesn’t face any consequences due to the experiment.
Non-experimental research refers to the type of research design that lacks the manipulation of the independent variable. Rather, researchers simply measure variables as they naturally occur in real life or in a lab. Although control groups are predominantly used in experimental research, there are two main types of control groups used in non-experimental research:
Matching in experimental design refers to comparing a specific participant in the treatment group with a specific participant in the experiment group. Both these participants will share similar characteristics. Therefore, each member in the treatment group will have an identical counterpart in the control group. This helps ensure that the treatment is the only cause of potential differences between the outcomes of each pair and of the two groups as a whole.
A quasi-experimental research design relies on non-random criteria for the allocation of subjects into different groups. This is different from a true experiment that relies on random assignment instead.
When we talk about invalid control groups, we refer to control groups that may share more differences than one with the experimental group. When your control group has differences from the treatment group, there will be variables that you haven’t accounted for that may lead to your experiment obtaining findings that are less credible and trustworthy.
Let’s look at the different ways in which the threats of invalid control groups can be minimized: